Breakout Labs wants to reshape the way early-stage science is funded, and to return scientific innovation to the hands of independent scientists, engineers, and inventors. Funding agreements will typically range from $50,000 to $350,000 but depend on the nature of the project.

Thiel has long been arguing that we're not seeing the rate of progress that we should, and I think he might be right. But what rate of progress are we seeing, and what rate of progress should we be seeing?

Back in 1871, Lord Kelvin observed:

Scientific wealth tends to accumulate according to the law of compound interest. Every addition to knowledge of the properties of matter supplies [the physical scientist] with new instrumental means for discovering and interpreting phenomena of nature, which in their turn afford foundations of fresh generalisations, bringing gains of permanent value into the great storehouse of [natural] philosophy.

But progress only seems to follow an exponential pattern on the surface. Not every advance provides the same utility to future research. Progress really is made up from many smaller paradigm shifts, that each create entirely new fields. Initially a new field makes possible a lot of follow-up results, but as it matures, it becomes increasingly harder to make significant discoveries within it. Eventually all the low-hanging fruit have been picked, and revolutionary advances give way to merely incremental improvements. A logistic S-Curve is a decent visualization of this progression:

Fig.2 S-Curve of a new paradigm

Only when we overlay a series of these paradigm shifts, do we move in the direction of exponential growth:

Fig.3 Multiple S-Curves overlayed

Thiel seems to argue that we're not producing these significant breakthroughs at a high enough rate anymore. Through institutionalism, centralization and standardization, we've homogenized the kind of research that can get funding. We've stymied radical experiments in favor of safe, predictable, incremental improvements. To continue making radical discoveries, we need to be willing to accept less centralized control over the direction of research.

True innovation is by definition hard to recognize. Anything that is obvious would already have been discovered. New ideas require rethinking and challenging what we believe to be true and absolute. It's tough for an outsider, and most innovators are outsiders, to garner the level of attention they need to get their ideas accepted. It's only after they've succeeded that we start to idolize their way of thinking, and look towards people that fit the same pattern for the next wave of innovation. But the previous generation of rebels often aren't the ones to recognize the significance of the next. By the very nature of innovation, every new wave will be unlike the previous one. Truly new ideas always look unworkable, boring, crazy, and absurd.

A good example of this we find in the history of game theory: The entire edifice of game theory rests on two theorems: von Neumann’s min-max theorem of 1928 and Nash’s equilibrium theorem of 1950. Like many great scientific ideas, from Newton’s theory of gravitation to Darwin’s theory of natural selection, Nash’s idea seemed initially too simple to be truly interesting, too narrow to be widely applicable, and, later on, so obvious that its discovery by someone was deemed all but inevitable. Today, Nash’s concept of equilibrium from strategic games is one of the basic paradigms in social sciences and biology, and earned Nash a Nobel Prize.
Still, the eminent von Neumann initially rejected Nash's ideas, just as Einstein had rejected one of Nash's earlier ideas, albeit more politely.

But Nash was lucky in that he didn't have to ask for permission to conduct his research. Since he was a mathematician and economist with a scholarship, he didn't require funds for experiments. Also Princeton at the time, was a fairly unique institution in that it subjected its students to a maximum of pressure but a wonderful minimum of bureaucracy. The department offered courses, true, but enrollment was a fiction, as were grades. Some professors put down all As, others all Cs, on their grade reports, but both were completely arbitrary. You didn’t have to show up a single time to earn them and students’ transcripts were, more often than not, works of fiction “to satisfy the Philistines.” More than that, not only could he pursue his own studies, but at Princeton he had access to von Neumann and Einstein and many other of the greatest minds of his time. Given that access, he was eventually able to find established mentors that helped him get his research accepted.

In many other sectors however, progress can't happen without initial funding. Also an increasing number of innovators, especially those residing outside of elite academic institutions, lack the level of access to the ears of the great minds in their fields. Worse, as the amount of research grows, and the world is becoming more interconnected, an ever increasing number of researchers and ideas are vying for the attention of a comparatively small count of established individuals that can't thoroughly study even a small fraction of them.

Today, for ideas and proposals to reach high-profile individuals that can support them, they must pass through layers of gatekeepers or committees. But gatekeepers and committees are notoriously bad at recognizing breakout brilliance - they always tend towards the safe average. Thus we're facing a structural problem, wherein the way we discover and promote worthwhile projects can no longer scale with the volume of project proposals produced. If we can't consume the research of our peers anymore, and we're not discovering the occasional brilliance therein, how can that briliance contribute to our own research, and thus fuel progress?

The challenge for Breakout Labs is going to be to find ways to recognize truly exciting projects - not just by being more bold, but by developing new methodologies to determine at scale, which radical ideas are worth funding. I'll argue in a follow-up post, that the most reasonable metric might be a measurement of an individual or team's ability to execute, rather than an assessment of the research idea itself.